column means:
## V1 V2 V3 V4 V5 V6
## 0.4642 0.2511 0.4557 0.5114 0.4700 0.4954
column ggcorr:
column means:
## V1 V2 V3 V4 V5 V6
## 0.4432 0.7291 0.4188 0.5136 0.4487 0.4963
column ggcorr:
column means:
## V1 V2 V3 V4 V5 V6
## -0.0209937 0.4779925 -0.0369365 0.0021539 -0.0212284 0.0008873
column ggcorr:
## Call:
## lda(dat, grouping = clas)
##
## Prior probabilities of groups:
## a c b
## 0.3027 0.3947 0.3027
##
## Group means:
## V1 V2 V3 V4 V5 V6
## a 0.4642 0.2511 0.4557 0.5114 0.4700 0.4954
## c 0.4432 0.7291 0.4188 0.5136 0.4487 0.4963
## b 0.4297 0.2675 0.4280 0.5104 0.4110 0.4682
##
## Coefficients of linear discriminants:
## LD1 LD2
## V1 -0.8696 -3.16249
## V2 13.0408 0.02112
## V3 -0.5433 -0.28177
## V4 -0.2805 1.69474
## V5 -0.7874 -4.52800
## V6 -2.6175 -0.77344
##
## Proportion of trace:
## LD1 LD2
## 0.9972 0.0028
## Standard deviations (1, .., p=6):
## [1] 0.2620 0.2207 0.1612 0.1460 0.1414 0.1379
##
## Rotation (n x k) = (6 x 6):
## PC1 PC2 PC3 PC4 PC5 PC6
## V1 -0.1581 0.1947 -0.5195 0.15941 0.1468 0.78758
## V2 -0.8088 -0.5633 0.1180 -0.00358 0.1161 0.03388
## V3 -0.2292 0.5179 0.3255 0.37085 0.6419 -0.15413
## V4 -0.2039 0.2958 -0.2303 -0.85315 0.2640 -0.14253
## V5 -0.2976 0.4400 0.5252 -0.14619 -0.5678 0.31337
## V6 -0.3718 0.3090 -0.5305 0.29633 -0.4010 -0.48611
Original variable cluster seperation:
Original vs MMP cluster seperation:
MMP cluster seperation: